Population-based incidence of proximal radial and ulnar fractures among adults in a Canadian metropolitan area
Bibliographic record
Abstract
Background The lack of North American population-based incidence studies of fractures of the proximal radius and ulna creates challenges in the assessment of outcomes and corresponding complication rates. Such data may help to establish consensus regarding optimal treatments. The present study's goal was to determine the population-based incidence of proximal radial and ulnar fractures in a large metropolitan area. Methods Over a 3-year period (April, 2002–March, 2005), cases of proximal radial and ulnar fractures were documented and classified according to the AO/OTA system. Overall, age-adjusted, age-specific, gender-specific, and fracture-specific rates were calculated according to patient demographic and 2001 Canadian census data. Rates were reported as per 10,000 persons per year. Results 1030 proximal radial and ulnar fractures were identified. Fractures occurred at an overall rate of 5.09 (95%CI: 4.78 to 5.40), while the age-adjusted incidence was 5.14 (95% CI: 5.05–5.23). The most common fracture types observed were B2.1 (simple articular fracture of radius, n=374), B1.1 (unifocal articular fracture of ulna, n=280), and A2.2 (simple extra-articular fracture of neck of radius, n=145). Fracture incidence was similar among all age groups (approximately five), with the exception of patients ages 80 years and older (8.70; 95% CI: 6.24–11.16). Males and females had similar fracture incidences at all ages. Conclusions As our results indicate similar incidences across age and gender groups, our data is likely generalizable to the general population, which may provide further insight into the assessment of outcomes and complication rates of such injuries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".